How will quantum impact the biotech industry?

The physics of atoms and the technology behind treating disease might sound like disparate fields. However, in the past few decades, advances in artificial intelligence, sensing, simulation and more have driven enormous impacts within the biotech industry.

Quantum computing provides an opportunity to extend these advancements with computational speedups and/or accuracy in each of those areas. Now is the time for enterprises, commercial organizations and research institutions to begin exploring how to use quantum to solve problems in their respective domains.

As a Partner in IBM’s Quantum practice, I’ve had the pleasure of working alongside Wade Davis, Vice President of Computational Science & Head of Digital for Research at Moderna, to drive quantum innovation in healthcare. Below, you’ll find some of the perspectives we share on the future in quantum compute in biotech.

What is quantum computing?

Quantum computing is a new kind of computer processing technology that relies on the science that governs the behavior of atoms to solve problems that are too complex or not practical for today’s fastest supercomputers. We don’t expect quantum to replace classical computing. Rather, quantum computers will serve as a highly specialized and complementary computing resource for running specific tasks.

A classical computer is how you’re reading this blog. These computers represent information in strings of zeros and ones and manipulate these strings by using a set of logical operations. The result is a computer that behaves deterministically—these operations have well-defined effects, and a sequence of operations resulting in a single outcome. Quantum computers, however, are probabilistic—the same sequence of operations can have different outcomes, allowing these computers to explore and calculate multiple scenarios simultaneously. But this alone does not explain the full power of quantum computing. Quantum mechanics offers us access to a tweaked and counterintuitive version of probability that allows us to run computations inaccessible to classical computers. 

Therefore, quantum computers enable us to evaluate new dimensions for existing problems and explore entirely new frontiers that are not accessible today. And they perform computations in a way that more closely mirrors nature itself.

As mentioned, we don’t expect quantum computers to replace classical computers. Each one has its strengths and weaknesses: while quantum will excel at running certain algorithms or simulating nature, classical will still take on much of the work. We anticipate a future wherein programs weave quantum and classical computation together, relying on each one where they’re more appropriate. Quantum will extend the power of classical. 

Unlocking new potential

A set of core enterprise applications has crystallized from an environment of rapidly maturing quantum hardware and software. What the following problems share are many variables, a structure that seems to map well to the rules of quantum mechanics, and difficulty solving them with today’s HPC resources. They broadly fall into three buckets:

• Advanced mathematics and complex data structures. The multidimensional nature of quantum mechanics offers a new way to approach problems with many moving parts, enabling better analytic performance for computationally complex problems. Even with recent and transformative advancements in AI and generative AI, quantum compute promises the ability to identify and recognize patterns that are not detectable for classical-trained AI, especially where data is sparse and imbalanced. For biotech, this might be beneficial for combing through datasets to find trends that might identify and personalize interventions that target disease at the cellular level.
• Search and optimization. Enterprises have a large appetite for tackling complex combinatorial and black-box problems to generate more robust insights for strategic planning and investments. Though further on the horizon, quantum systems are being intensely studied for their ability to consider a broad set of computations concurrently, by generating statistical distributions, unlocking a host of promising opportunities including the ability to rapidly identify protein folding structures and optimize sequencing to advance mRNA-based therapeutics.
• Simulating nature. Quantum computers naturally re-create the behavior of atoms and even subatomic particles—making them valuable for simulating how matter interacts with its environment. This opens up new possibilities to design new drugs to fight emerging diseases within the biotech industry—and more broadly, to discover new materials that can enable carbon capture and optimize energy storage to help industries fight climate change.

At IBM, we recognize that our role is not only to provide world-leading hardware and software, but also to connect quantum experts with nonquantum domain experts across these areas to bring useful quantum computing sooner. To that end, we convened five working groups covering healthcare/life sciences, materials science, high-energy physics, optimization and sustainability. Each of these working groups gathers in person to generate ideas and foster collaborations—and then these collaborations work together to produce new research and domain-specific implementations of quantum algorithms.

As algorithm discovery and development matures and we expand our focus to real-world applications, commercial entities, too, are shifting from experimental proof-of-concepts toward utility-scale prototypes that will be integrated into their workflows. Over the next few years, enterprises across the world will be investing to upskill talent and prepare their organizations for the arrival of quantum computing.

Today, an organization’s quantum computing readiness score is most influenced by its operating model: if an organization invests in a team and a process to govern their quantum innovation, they are better positioned than peers that focus just on the technology without corresponding investment in their talent and innovation process.  IBM Institute for Business Value | Research Insights: Making Quantum Readiness Real

Among industries that are making the pivot to useful quantum computing, the biotech industry is moving rapidly to explore how quantum compute can help reduce the cost and speed up the time required to discover, create, and distribute therapeutic treatments that will improve the health, the well being and the quality of life for individuals suffering from chronic disease. According to BCG’s Quantum Computing Is Becoming Business Ready report: “eight of the top ten biopharma companies are piloting quantum computing, and five have partnered with quantum providers.”

Partnering with IBM

Recent advancements in quantum computing have opened new avenues for tackling complex combinatorial problems that are intractable for classical computers. Among these challenges, the prediction of mRNA secondary structure is a critical task in molecular biology, impacting our understanding of gene expression, regulation and the design of RNA-based therapeutics.

For example, Moderna has been pioneering the development of quantum for biotechnology. Emerging from the pandemic, Moderna established itself as a game-changing innovator in biotech when a decade of extensive R&D allowed them to use their technology platform to deliver a COVID-19 vaccine with record speed. 

Given the value of their platform approach, perhaps quantum might further push their ability to perform mRNA research, providing a host of novel mRNA vaccines more efficiently than ever before. This is where IBM can help. 

As an initial step, Moderna is working with IBM to benchmark the application of quantum computing against a classical CPlex protein analysis solver. They’re evaluating the performance of a quantum algorithm called CVaR VQE on randomly generated mRNA nucleotide sequences to accurately predict stable mRNA structures as compared to current state of the art. Their findings demonstrate the potential of quantum computing to provide insights into mRNA dynamics and offer a promising direction for advancing computational biology through quantum algorithms. As a next step, they hope to push quantum to sequence lengths beyond what CPLEX can handle.

This is just one of many collaborations that are transforming biotech processes with the help of quantum computation. Biotech enterprises are using IBM Quantum Systems to run their workloads on real utility-scale quantum hardware, while leveraging the IBM Quantum Network to share expertise across domains. And with our updated IBM Quantum Accelerator program, enterprises can now prepare their organizations with hands-on guidance to identify use cases, design workflows and develop utility-scale prototypes that use quantum computation for business impact. 

The time has never been better to begin your quantum journey—get started today.

Source: ibm.com

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At what cost can we simulate large quantum circuits on small quantum computers?

One major challenge of near-term quantum computation is the limited number of available qubits. Suppose we want to run a circuit consisting of 400 qubits, but we only have 100-qubit devices available. What do we do?

Over the course of the past year, the IBM Quantum team has begun researching a host of computational methods called circuit knitting. Circuit knitting techniques allow us to partition large quantum circuits into subcircuits that fit on smaller devices, incorporating classical simulation to “knit” together the results to achieve the target answer. The cost is a simulation overhead that scales exponentially in the number of knitted gates.

Circuit knitting will be important well into the future. Our quantum hardware development team is focused on scaling by connecting smaller processors via classical, and then via quantum links. Due to this planned hardware architecture, circuit knitting will be useful in the near future as we run problems on classically parallelized quantum processors. Techniques that boost the number of available qubits will also be relevant far into the future.

Figure 1: Circuit knitting example: The nonlocal circuit on the left acting on A⊗B can be simulated with local circuits acting only on A or B on the right followed by classical postprocessing.

But first, our team needed to understand how much of a benefit these methods can offer, especially when we knew that the simulation overhead scales exponentially with the number of gates acting between these subcircuits.

We are currently investigating whether classical communication between local quantum computers can help to lower the simulation overhead — as you might see on a pair of classically parallelized IBM Quantum “Heron” processors. Specifically, we realized circuit knitting via a method that has previously gained interest in the fields of error mitigation and classical simulation algorithms, called the quasiprobability simulation technique.

The 133-qubit “Heron” processor, slated for 2023.

We consider three settings to simulate a non-local circuit with local operations. In the first, the two quantum computers can only run their own local operations on their subcircuits without communication between them. In the second the two computers can realize those local operations, with the added ability to send classical information in one direction — from \AlphaA to \BetaB, but not from \BetaB to \AlphaA. In the third, the two quantum computers can run their own local quantum operations and send classical information in either direction between them.

In the local and one-way classical communication settings, one does not necessarily require two separate quantum computers. Instead, one can run the two subcircuits in sequence on the same device. The classical communication in the one-way setting can then be simulated by classically storing the bits sent from \AlphaA and \BetaB. 

Figure 2: Graphical overview of the three scenarios considered to run a nonlocal operation. LO refers to local operations; LO & one way CC refers to local operations and one way classical communication; LOCC refers local operations and classical communication.

In contrast, the two-way communication setting requires two quantum computers that exchange classical information in both directions. We show that for circuit knitting based on quasiprobability simulation, the three settings mentioned above all have a different sampling overhead when applied to circuits with multiple instances of the same non-local gate. 

Our results, available on arXiv, demonstrate that two-way communication can considerably reduce the simulation overhead. For circuits containing n CNOT gates connecting each subcircuit, the incorporation of classical information exchange between the subcircuits reduces the simulation overhead from O(9n) to O(4n) — a reduction that is substantial in practice. It allows us to cut considerably more CNOT gates — that is, the gates that entangle the qubits — for a given fixed simulation overhead.

On a technical level, our results are based on the insight that a simultaneous local preparation of two maximally entangled states, called Bell pairs, is more efficient than locally preparing a single Bell pair twice. The reason is that for a joint preparation we can make use of entanglement between the local subsystems, which is not possible if we prepare the two Bell pairs separately. Using the idea of gate teleportation we can then convert Bell pairs into CNOT gates under local operations and classical communication.

Figure 3: Graphical explanation of how to realize two CNOT gates in a LOCC setting via gate teleportation. By generating the two Bell pairs simultaneously (instead generating twice a single Bell pair), we can reduce the total simulation overhead.

Our results show that classical communication between locally separated quantum computers is beneficial when performing large computations that exceed the number of qubits each quantum device individually has.

Source: ibm.com

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What Is Quantum-Safe Cryptography, and Why Do We Need It?

How to prepare for the next era of computing with quantum-safe cryptography.

Cryptography helps to provide security for many everyday tasks. When you send an email, make an online purchase or make a withdrawal from an ATM, cryptography helps keep your data private and authenticate your identity.

Today’s modern cryptographic algorithms derive their strength from the difficulty of solving certain math problems using classical computers or the difficulty of searching for the right secret key or message. Quantum computers, however, work in a fundamentally different way. Solving a problem that might take millions of years on a classical computer could take hours or minutes on a sufficiently large quantum computer, which will have a significant impact on the encryption, hashing and public key algorithms we use today. This is where quantum-safe cryptography comes in.

According to ETSI, “Quantum-safe cryptography refers to efforts to identify algorithms that are resistant to attacks by both classical and quantum computers, to keep information assets secure even after a large-scale quantum computer has been built.”

What is quantum computing?

Quantum computers are not just more powerful supercomputers. Instead of computing with the traditional bit of a 1 or 0, quantum computers use quantum bits, or qubits (CUE-bits). A classical processor uses bits to perform its operations. A quantum computer uses qubits to run multidimensional quantum algorithms. Groups of qubits in superposition can create complex, multidimensional computational spaces. Complex problems can be represented in new ways in these spaces. This increases the number of computations performed and opens up new possibilities to solve challenging problems that classical computers can’t tackle.

There are many exciting applications in the fields of health and science, like molecular simulation that has the potential to speed up the discovery of new life-saving drugs. The problem is, however, quantum computers will also be able to solve the math problems that give many cryptographic algorithms their strength.

How will quantum computing impact cryptography?

Two of the main types of cryptographic algorithms in use today for the protection of data work in different ways:

◉ Symmetric algorithms use the same secret key to encrypt and decrypt data.

◉ Asymmetric algorithms, also known as public key algorithms, use two keys that are mathematically related: a public key and a private key.

The development of public key cryptography in the 1970s was revolutionary, enabling new ways of communicating securely. However, public key algorithms are vulnerable to quantum attacks because they derive their strength from the difficulty of solving the discrete log problem or factoring large integers. As discovered by mathematician Peter Shor, these types of problems can be solved very quickly using a sufficiently strong quantum computer, so in the case of asymmetric or public key cryptography, we need new math that will stand up to quantum attacks because today’s public key algorithms will be completely broken.

Grover’s Algorithm, devised by computer scientist Lov Grover, is a quantum search algorithm. Using Grover’s algorithm, some symmetric algorithms are impacted and some are broken. Key size and message digest size are important considerations that will factor into whether an algorithm is quantum-safe or not. For example, use of Advanced Encryption Standard (AES) with 256-bit keys is considered quantum-safe but Triple DES (TDES) can be broken no matter the key size.

What is being done to address future quantum threats?

The good news is that researchers and standards bodies are moving to address the threat. The National Institute of Standards and Technology (NIST) initiated a Post-Quantum Cryptography Standardization Program to identify new algorithms that can resist threats posed by quantum computers.

After three rounds of evaluation, NIST has identified seven finalists. They plan to select a small number of new quantum-safe algorithms early this year and have new quantum-safe standards in place by 2024. As part of this program, IBM researchers have been involved in the development of three quantum-safe cryptographic algorithms based on lattice cryptography that are in the final round of consideration: CRYSTALS-Kyber, CRYSTALS-Dilithium and Falcon.

How should enterprises be preparing to adopt quantum-safe cryptography?

Fortunately, we have time to implement quantum-safe solutions before the advent of large-scale quantum computers — but not much time. Moving to new cryptography is complex and will require significant time and investment. We don’t know when a large-scale quantum computer capable of breaking public key cryptographic algorithms will be available, but experts predict that this could be possible by the end of the decade.

Also, hackers can harvest encrypted data today and hold it for later when they can decrypt it using a quantum computer, so sensitive data with a long lifespan is already vulnerable. Organizations in the United States and Germany have already issued requirements for government agencies to follow regarding quantum-safe cryptography. BSI, a German federal agency, requires the use of hybrid schemes — where both classical and quantum-safe algorithms are used — for protection in high-security applications. The White House issued a memo requiring federal agencies to begin quantum-safe modernization planning.

How can IBM help?

As we prepare for a quantum world, IBM is committed to developing and deploying new quantum-safe cryptographic technology. Trusted hardware platforms will play a critical role in the adoption of quantum-safe cryptography. And IBM Z has already begun the modernization process. IBM z15 introduced lattice-based digital signatures within the system for digital signing of audit records within z/OS. IBM z15 also provided the ability for application developers to begin experimenting with quantum-safe lattice-based digital signatures. Because we’ve already begun the process, this helps us understand the implications of moving to new algorithms so we can pass on insights about the topic to our clients.

Preparing to adopt quantum-safe standards

When meeting with clients getting started on their journey to quantum safety, we share a few of the key milestones to help them get ready to adopt new quantum-safe standards:

◉ Discover and classify data: The first step involves classifying the value of your data and understanding compliance requirements. This helps you create a data inventory.

◉ Create a crypto inventory: Once you have classified your data, you will need to identify how your data is encrypted, as well as other uses of cryptography to create a crypto inventory that will help you during your migration planning. Your crypto inventory will include information like encryption protocols, symmetric and asymmetric algorithms, key lengths, crypto providers, etc. 

◉ Embrace crypto agility: The transition to quantum-safe standards will be a multi-year journey as standards evolve and vendors move to adopt quantum-safe technology. Use a flexible approach and be prepared to make replacements. Implement a hybrid approach as recommended by industry experts by using both classical and quantum-safe cryptographic algorithms. This maintains compliance with current standards while adding quantum-safe protection.

Many clients across industries have already started experimenting with new quantum-safe algorithms in order to assess the impact of new quantum-safe standards on their businesses:  

◉ Automotive: Clients in the automotive industry use public key technology in connected cars for vehicle-to-everything (V2X) communications and to verify the integrity of the firmware loaded into vehicles. The cars they are designing today will be on the road well into the future, so they are on a tight timeline to adopt quantum-safe technology. Because vehicles have hardware resource constraints, it is critical that automotive clients model and test new quantum-safe algorithms now to make sure they can accommodate the larger key sizes in their use cases.   

◉ Banking: Clients in the banking industry rely heavily on symmetric cryptography to ensure the confidentiality of data in core banking applications. There are many data retention and data confidentiality regulations and agreements that these clients must adhere to, such as retaining tax records for 7–10 years and keeping trade secrets confidential for up to 50 years. Adversaries are starting their attacks today with the intent of disclosing this type of confidential data in the future, so many banking clients have started creating data and crypto inventories to adopt quantum-safe protection for highly sensitive data. Banks also rely on public key cryptography, for example, in digital signatures used for authentication and software verification. It’s important for banking clients to begin modeling new quantum-safe algorithms to understand performance implications and prepare to adopt new standards as they evolve.

Source: ibm.com

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IBM Quantum’s Open Science Prize returns with a quantum simulation challenge

IBM Quantum is excited to announce the second annual Open Science Prize — an award for those who can present an open source solution to some of the most pressing problems in the field of quantum computing. Submissions are open now, and must be received by April 16, 2022.

This year, the challenge will feature one problem from the field of quantum simulation, solvable through one of two approaches. The best open source solution to each approach will receive a $40,000 prize, and the winner overall will receive another $20,000.

Simulating physical systems on quantum computers is a promising application of near-term quantum processors. This year’s problem asks participants to simulate a Heisenberg model Hamiltonian for a three-particle system on IBM Quantum’s 7-qubit Jakarta system. The goal is to simulate the evolution of a known quantum state with the best fidelity as possible using Trotterization.

Researchers use the Heisenberg model to study a variety of physical systems involving interacting particles with spins. Quantum computers are useful tools to simulate these models because you can represent the spin states of particles as the computational states of qubits.

But tackling this Hamiltonian can prove challenging, since different subsets of qubits in the same system don’t commute — that is, you can’t measure subsets of the problem simultaneously to a high precision, due to the Uncertainty Principle.

Trotterization allows us to simulate these kinds of systems by quickly switching between the non-commuting parts.

We picked this problem because the Heisenberg model is ubiquitous and relatively simple — therefore, it’s a great place to start for those just dipping a toe into quantum simulation. But also, given the model’s ubiquity, any solution that betters our ability to simulate it will have broad impact on the field of quantum simulation overall.

Team up to win

1. Participants can team up into groups of up to five, and can choose to solve the problem in one of two ways:

2. Either use Qiskit Pulse, that is, the Qiskit module that allows users pulse-level control over quantum quantum gates,

Or try to solve the problem using Qiskit defaults.

We encourage each team to push outside of their members’ comfort zone and try whichever method they think is best suited to solve the problem. Although Qiskit Pulse offers more detailed control of the qubits, there are advantages and disadvantages to both approaches.

Closing the door on last year’s prize

Last year’s Open Science Prize was a hit, as participants used Qiskit Pulse to simulate two challenging outstanding problems whose solutions could help advance the field of quantum computation. We had more than 30 submissions to two cutting edge research challenges that were, and still are, open questions in the field. Participants used Qiskit to run more than 6 billion circuits on IBM Quantum’s 7-qubit Casablanca system. We hope to see even more participation this year from across the quantum computing community.

Source: ibm.com

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Creating a global ecosystem for the Quantum industry

Quantum computing will transform key sectors of many industries in the years ahead and help us tackle some of the world’s most complex challenges in energy, materials and chemistry, finance, and elsewhere — and perhaps areas that we haven’t considered yet. This is the Quantum Decade: the integration of quantum computing, AI, and high performance computing into hybrid multi-cloud workflows will drive the most significant computing revolution in decades and enterprises will evolve from analyzing data to discovering new ways to solve problems.

We are seeing a growing community and industry with real technology and enormous excitement from venture capital, major technology players, and governments to create the next wave of information technology. At IBM’s recent Chief Data and Technology Officer Summit, I had an exciting conversation with Darío Gil, Senior Vice President, IBM and Director of Research around quantum computing and its current stage, and how IBM continues to rapidly innovate quantum hardware and software design, building ways for quantum and classical workloads to empower each other.

Building quantum computers has been a dream in computer science for many decades. We are finally building systems that behave and operate according to the laws of quantum mechanics, and making them widely accessible to a broad community. We are building the future of quantum computing together. In May 2016, IBM was the first company with a small quantum computer on the cloud. Today, we have 27 systems on the cloud with a community of over 350,000 users worldwide, running more than 2 billion quantum circuits every day.

At this year’s Quantum Summit, we debuted our 127-qubit Eagle quantum processor, the first IBM quantum processor to contain more than 100 operational and connected qubits. The increased qubit count allows users to explore problems at a new level of complexity, such as optimizing machine learning or modeling new molecules and materials. What is essential is how we have done it: solving some very hard problems while outperforming in three attributes: scale, quality, and speed.

Engaging the Developer Community 

Allowing developers to be shielded from the complexity of the quantum hardware and to seamlessly integrate classical and quantum computing is another breakthrough. Quantum Serverless, a new programming model for leveraging quantum and classical resources, offers users the advantage of quantum resources at scale without worrying about the intricacies of the hardware.

Discover how you can be quantum-ready and how this bleeding-edge technology can help you and your business thrive the moment quantum computers come of age here.

2021 CDO and CAO Award Winners

If you missed our CDO/CTO Summit, we invite you to watch the replay. In addition to discussing quantum computing and how IBM clients embrace it, we heard from leading industry experts their reflections on various topics around data, AI, privacy, ethics, governance, risk, and innovation. We also announced the winners of the U.S. 2021 “Chief Data Officer of the Year”: Eileen Vidrine, Chief Data Officer at The United States Department of the Air Force, and the U.S. 2021 “Chief Analytics Officer of the Year”: Sol Rashidi, Senior Vice President, Global Data & Analytics and Chief Analytics Officer at The Estée Lauder Companies Inc.

Source: ibm.com

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IBM’s quantum computers: an optimal platform for condensed matter physics research

While condensed matter physicists often must rely on large collaborations or costly hardware to run their experiments, our cloud-based quantum processors allow users to make groundbreaking advances in condensed matter physics with little more than their laptop and a user account with IBM Quantum.

Matters’ constituent particles can interact in a variety of ways based on their intrinsic properties. These interactions manifest themselves as materials with properties that serve functions in every aspect of our lives — whether solid, liquid, or gas. Some interactions between particles, however, can give rise to exotic properties and phases of matter, like superconductivity or ferromagnetism. Condensed matter physicists study how inter-particle interactions give rise to these interesting behaviors. And the physics of these interactions is described by the laws of quantum mechanics, which was one of the first motivations for building and simulating them on a quantum computer.

Condensed matter physics has important implications for our understanding of nature and the development of new technologies. Advances made by condensed matter physicists have led to seminal inventions, like the development of the transistor, and the building blocks of IBM Quantum processors’ superconducting qubits with Josephson junctions.

Given the importance of furthering our understanding of matter, we’re excited that IBM Quantum systems make ideal laboratories to study condensed matter physics. And while condensed matter physicists often must rely on large collaborations or costly hardware to run their experiments, our cloud-based quantum processors enable users to make potentially groundbreaking advances in condensed matter physics with little more than their laptop and a user account with IBM Quantum.

Spinning up real world condensed matter physics research

In fact, a small team of researchers employing even today’s noisy quantum computers can make a valuable impact. An active area studied by condensed matter physicists is the dynamics of interacting spin systems. Spin is a crucial property of elementary particles that could be described by the clockwise or counter-clockwise spinning of toy tops, but with a quantum take.

The two states of a quantum bit form a natural analogue to the “up” and “down” spins, and interactions between spins can be easily toggled by our systems’ control pulses. The connectivity of our qubits therefore allows users to naturally simulate the dynamics of spin lattices, and explore how the collective behavior of spins change under the influence of external forces.

For example, a paper by researchers at the Autonomous University of Madrid (UAM) simulates the dynamics of a one-dimensional Ising model — essentially, a line of particles in one of the two spin states that could interact only with their neighbors — in the presence of external magnetic fields both parallel and perpendicular to the system. They recreated this system on the ibmq_paris system’s 27-qubit processor.

Another study by researchers at Lawrence Berkeley National Laboratory simulated another canonical spin system described by the Heisenberg model — also done on the ibmq_paris system. In each case, the teams found that they could accurately calculate relevant properties of the systems that they were studying, and could significantly enhance the quality of their simulations with error mitigation techniques, even on existing noisy processors.

Crucially, these teams were able to run their simulations without any specialized equipment; they simply had to run their quantum programs on a cloud-based IBM Quantum computing system. Dozens of papers published on the arXiv physics pre-print server, including our own experiments with our 27-qubit processors, have demonstrated the power of IBM Quantum processors in simulating spin dynamics. Even noisy quantum computers may soon have the potential to provide a quantum advantage over classical methods for some condensed matter problems.

Using a quantum computer to understand phases of matter that act like a crystal in time

Other researchers are using quantum computers to study phases of matter beyond what we can create in the lab. This past May, two researchers at the University of Melbourne reported evidence for the much-heralded time crystal on arXiv — using ibmq_manhattan’s and ibmq_brooklyn’s 65-qubit processors to simulate a chain of 57 driven spins with nearest-neighbor interactions. The traditional crystal is a spatial crystal, such as matter composed of a lattice of atoms in a stable, preferred structure in space. The idea of the time crystal posits that perhaps there exists some phases of matter that act like a crystal in time, with states that have a periodicity in time. Physicists have been actively studying driven spin systems as a platform for the realization of such phases of matter.

Source: ibm.com

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IBM Quantum systems accelerate discoveries in science

Today, computation is central to the way we carry out the scientific method. High-performance computing resources help researchers generate hypotheses, find patterns in large datasets, perform statistical analyses, and even run experiments faster than ever before. Logically, access to a completely different computational paradigm — one with the potential to perform calculations intractable for any classical computer — could open up an entirely new realm for scientific discovery.

As quantum computers extend our computational capabilities, so too do we expect them to extend our ability to push science forward. In fact, access to today’s limited quantum computers has already provided benefits to researchers worldwide, offering an unprecedented look at the inner workings of the laws that govern how nature works, as well as a new lens through which to approach problems in chemistry, simulation, optimization, artificial intelligence, and other fields.

Here, we demonstrate the utility of IBM Quantum hardware as a tool to accelerate discoveries across scientific research, as shown at the American Physical Society’s March Meeting 2021. The APS March Meeting is the world’s largest physics conference, where researchers present their latest results to their peers and to the wider physics community. As a leading provider of quantum computing hardware, IBM’s quantum systems powered 46 non-IBM presentations in order to help discover new algorithms, simulate condensed matter and many-body systems, explore the frontiers of quantum mechanics and particle physics, and push the field of quantum information science forward overall. With this year’s APS March Meeting in mind, we believe that research access to quantum hardware — both on-site and via the cloud — will become a core driver for exploration and discovery in the field of physics in the coming years.

IBM’s quantum systems powered 46 non-IBM presentations in order to help discover new algorithms, simulate condensed matter and many-body systems, explore the frontiers of quantum mechanics and particle physics, and push the field of quantum information science forward overall.

IBM Quantum systems

At IBM Quantum, we build universal quantum computing systems for scientists, engineers, developers, and businesses. Our initiative operates a fleet of over two dozen full-stack quantum computing systems ranging from 1 to 65 qubits based on the transmon superconducting qubit architecture. These systems incorporate state of the art control electronics and a continually evolving software in order to offer the best-performing quantum computing services in the world. Our team released our development roadmap, demonstrating how we plan not only to scale processors up, but how to turn these devices into transformative computational tools.

IBM offers access to its quantum computing systems through several avenues. Our flagship program is our IBM Quantum Network, including our hubs, which collaborate with IBM on advancing quantum computing research, our industry partners, who explore a broad set of potential applications, and our members, who seek to build their general knowledge of quantum computing. At the broadest level, members of our community use the IBM Quantum Composer and IBM Quantum Lab programming tools, as well as the Qiskit open source software development kit to build and visualize quantum circuits and run quantum experiments on a dozen smaller devices. Researchers can also receive priority system access through our IBM Quantum Researchers Program.

Through the Network, the Researchers Program, and the quantum programming tools available to the broader community, IBM offers a range of support in order to facilitate the research and discovery process. This includes, but is not limited to, direct collaboration with our quantum researchers on projects, consultation on potential topic-specific use cases, and fostering the open source community passionate to advance the field of quantum computation.

Developing an ecosystem around cloud-based quantum access

As quantum computers mature, their physical requirements will necessitate that most users remotely access them and can program them in a frictionless way — that is, reap their benefits without needing to be a quantum mechanics expert. Quantum computing outfits across the industry are developing quantum systems in anticipation of this developing ecosystem. Access to these cloud-based computers will be of chief importance to three key developer segments: quantum kernel developers, seeking to understand quantum computers and their underlying mechanics to the level of logic circuits; quantum algorithm developers, employing these circuits to find potential advantages over existing classical computing algorithms and to push the limits of computing overall, and model developers, who apply these algorithms to perform research on real-world use cases in fields like physics, chemistry, optimization, machine learning, and others.

While IBM is developing our own ecosystem through accessible services on the IBM Cloud, we think that quantum access is important beyond our own communities. We’ve developed Qiskit to run application modules on any quantum computing platform, even other architectures such as trapped-ion devices. Ultimately, our goal is to democratize access to quantum computing, while providing the best hardware and expertise to all of those who hope to do research with and on our devices.

Using quantum computers for discovery, today

The multitude of presentations leveraging IBM Quantum at the APS March Meeting demonstrate not only adoption of IBM’s quantum computers as a platform for research by institutions outside of IBM, but more importantly, that the ability to access and run programs on these devices via the cloud is already advancing science and research today. Experiments on our systems spanned each of our projected developer segments, from kernel developers researching quantum computing itself, algorithm developers, as well as model developers employing quantum computing as a means to approach other problems in physics and beyond.

Quantum simulation

The innately quantum nature of qubits means that even noisy quantum computers serve as powerful analog and digital simulators of quantum mechanics, such as those studied in quantum many-body and condensed matter physics. Quantum computers are arguably already providing a quantum advantage to researchers in these fields, who are able to tackle problems with a simulator whose properties more closely align with the systems they wish to study versus a classical computer. IBM Quantum systems played a central role in many of these cutting-edge studies at APS March.

For example, in her presentation, “Scattering in the Ising Model with the Quantum Lanczos Algorithm”, Oak Ridge National Lab’s Kubra Yeter Aydeniz simulated one-particle propagation and two-particle scattering in the ubiquitous one-dimensional Ising model of particles in one of two spin states, here with periodic boundary conditions. Her team employed an algorithm to calculate the energy levels and eigenstates of the system, gathering information on particle numbers for spatial sites and transition amplitudes as well as the transverse magnetization as a function of time.

Benchmarking and characterizing noisy quantum systems

As quantum computers grow in complexity, simulating their results classically will grow more difficult, in turn hampering our ability to tell whether they’ve successfully run a circuit. Researchers are therefore devising methods to characterize and benchmark the performance of near-term quantum computers overall — and hopefully develop methods that will continue to be applicable as quantum computers increase in size and complexity. A series of APS March talks demonstrated benchmarking methods applied to IBM’s quantum devices.

In one such talk, “Scalable and targeted benchmarking of quantum computers” Sandia National Lab’s Timothy Proctor presented his scalable and flexible benchmarking technique that expanded on the IBM-devised Quantum Volume metric, in order to capture the potential tradeoff between increasing a circuit’s depth (the number of time-steps worth of gates) versus its width (the number of qubits employed). By employing randomized mirror circuits — those composed of a random series of one- and two-qubit operations, followed by the inverse of those operations — the team developed a benchmarking strategy that would efficiently work on quantum computers of 100s or perhaps 1,000s of qubits.

Algorithmic Discovery

We hope that, one day, quantum computers will employ superposition, entanglement, and interference in order to provide new ways to solve traditionally difficult problems. Today, scientists are working to develop algorithms that will provide those potential speedups—with an eye toward what sorts of benefits they may gather from algorithms they can run on present-day devices. IBM’s quantum devices served as the ideal testbed for teams looking for a system with which to develop hardware-aware algorithms.

For example, in the NSF-funded work “Rodeo Algorithm for Quantum Computation”, Jacob Watkin presented a new approach to the ubiquitous quantum phase estimation algorithm called the Rodeo Algorithm, targeted at near-term quantum devices. The algorithm, meant to generalize the famous Kitaev Phase Estimation Algorithm, employs stochastically varying phase shifts in order to achieve results at short gate depths.

Advancing Quantum Computing

Perhaps the most popular use of IBM’s quantum systems at the APS March Meeting was as a foundation upon which to study the inner workings of quantum devices, including characterizing noise, testing the fidelity of the chips, developing error correction and mitigation strategies, and other research meant to advance the field as a whole. We hope that the advances gleaned from studying our devices will benefit the field overall.

In “Error mitigation with Clifford quantum-circuit data”,  Piotr Czarnik from Los Alamos National Laboratory proposed a new error mitigation method for gate-based quantum computers. The method begins by generating training data from quantum circuits built only from Clifford group gates, then creates a linear fit to the data that can predict noise free observables for arbitrary noisy circuits. Czarnik’s team demonstrated an order-of-magnitude error reduction for a ground state energy problem by running their error mitigation strategy on the 16 qubit ibmq_melbourne system.

…And more

Access to a controllable quantum system offers researchers a new way to think about problems across physics. For example, in “Collective Neutrino Oscillations on a Quantum Computer”, Shikha Bangar demonstrated that quantum resources can serve as an efficient way to represent a particle physics system — collective neutrino oscillations. Meanwhile, in “Quantum Sensing Simulation on Quantum Computers using Optimized Control”, Paraj Titum from The Johns Hopkins University Applied Physics Laboratory developed new protocols to detect signals over background noise, and demonstrated the protocol on an IBM quantum computer.

The Future

The IBM Quantum team is thrilled knowing that our hardware is accelerating scientific progress around the world—and we continue to push progress on our own hardware in order to keep these discoveries flowing. The APS March meeting also served as a venue for our researchers to present some of the ideas they’re developing for future quantum systems, including advanced packaging technologies, novel qubit coupling architectures, and even qubits a tiny fraction of the size of our current transmons.  We also used the very same IBM Quantum systems to drive progress in improving quantum volume, demonstrations of algorithms and quantum advantage, and exploration of dynamic circuits and quantum error correction. The interplay between the end-users of IBM’s systems and the researchers developing the next generation of processors helps keep IBM’s devices cutting-edge and relevant in the months and years to come.

Access to quantum computing systems is advancing science, even in this early era of noisy quantum computers. This applies to more than just IBM’s systems; scientists at the APS March meeting presented results based on access to other superconducting architectures such as Rigetti’s, as well as trapped-ion qubit systems like those built by Honeywell. Our analysis of the 2021 APS March Meeting’s results demonstrates that investment into and use of existing cloud-based quantum computing platforms provides researchers with a powerful tool for scientific discovery. We expect the pace of discovery to accelerate as quantum computing systems and their associated cloud-based quantum ecosystem matures.

Source: ibm.com

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IBM’s innovation: Topping the US patent list for 28 years running

Granted to my IBM colleagues and myself in 2005, it was for a topcoat waterproof material for a photoresist — a light-sensitive substance used to make circuit patterns for semiconductor chips. It was a proud moment for me — especially as I knew that this patent contained novel capabilities that were critical for a brand-new technology called immersion lithography. This technology soon became the basis for how all advanced chips are manufactured, even to this date.

I also knew it had contributed to IBM’s patent leadership that year. Just like during the 13 years before and 15 years after, IBM has been getting more patents granted than any other company in the US.

For me, this patent leadership symbolizes much more than just the mere fact of being at the top. A patent is evidence of an invention, protecting it through legal documentation, and importantly, published for all to read. The number of patents we produce each year — and in 2020, it was more than 9,130 US patents — demonstrates our continuous, never-ending commitment to research and innovation. We are actively planting the research seeds of the bleeding edge technological world of tomorrow. Our most recent patents span artificial intelligence (AI), hybrid cloud, cyber-security and quantum computing. It doesn’t get more future-looking than this.

The US patent system goes back to the very dawn of our nation. It is detailed in the Constitution, enabling the Congress to grant inventors the exclusive right to their discoveries for a specific period of time. It is an assurance designed to motivate inventors to keep innovating.

One might argue against having patents that don’t get immediately turned into commercial products. But I disagree. Inventing something new is similar to putting forward a well thought out theory that may, one day, be verified experimentally. Perhaps not straight away, but it’s still vital to have theories to enhance our overall understanding of a field and to keep progress going. Having future-looking patents is just as important as those aimed at products of today, and a broad portfolio of scientific advances always ends up contributing to waves of innovation.

Patents drive innovation and a nation’s economic performance. Over the years, they have given us breakthrough technologies such as the laser, self-driving cars, graphene and solar panels. We at IBM have developed and patented such widely used products as the automated teller machine (ATM), speech recognition technology, B2B e-commerce software with consumer-like shopping features for processing business orders, the hard disk drive, DRAM (the ubiquitous memory that powers our phones and computers), and even the famous floppy disk that’s now history, to name just a few.

Tackling the world’s problems

A patent’s assurance of the protection of inventions is a key reason why companies invest billions of dollars in research and development. This results in scientists and engineers in different companies trying to find the best, original solutions to the world’s problems, paving the way for new and better products. And we haven’t run out of global problems to solve, far from it. Innovation is what helps us deal with pandemics, tackle global warming, address energy and food shortages, and much more.

Last year, just in the field of AI, our researchers received more than 2,300 patents. To take two examples among many: a novel way to search multilingual documents using natural language processing, and an ultra-efficient system for transferring image data taken by an on-vehicle camera. These both speak to the innovation and original thinking from our inventors in AI.

In cloud, we received about 3,000 patents, many focusing on data processing categorizations that can help bring services to the edge. In cyber-security, I’d like to single out patents in fully homomorphic encryption — an area of cryptography where computations are made on data that stays encrypted at all times. With so many data leaks jeopardizing the privacy of our medical, genomic, financial and other sensitive records, secure encryption is more important than ever.

Finally, there is quantum computing. This next-generation technology is getting ever better. I am convinced that in the near future, products relying on quantum computation will be an integral part of our daily lives. By inventing and patenting those products today, we are ensuring our quantum future.

One of our quantum computing patents deals with running molecular simulations on a quantum computer. Performing such simulations faster and across a much wider molecular space than a classical computer can ever do could help us design new molecules for novel drugs or catalysts. Another patent addresses the use of quantum computing in finance, to run risk analysis more precisely and efficiently than ever before.

That’s far from all. Quantum computers of today are ‘noisy’ — meaning that the quantum bits, or qubits, they rely on get easily affected by any external disturbances. Many of our patents detail ways to make qubits much more stable and even suggest approaches to correct the remaining errors in future stable qubits, offering a path to realize quantum error-correction and unleash the power of quantum computers to solve the currently unsolvable.

I’ll end with a reflection. A vibrant culture of innovation combines patenting, publishing, contributing to open-source, and active in-market experimentation and discovery. All are needed, fueled by the joy that innovators experience with the spark of novel ideas, and the desire to bring them to life.

Source: ibm.com

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How to measure and reset a qubit in the middle of a circuit execution

IBM Quantum is working to bring the full power of quantum computing into developers’ hands in the next two years via the introduction of dynamic circuits, as highlighted in our recently released Quantum Developer Roadmap. Dynamic circuits are those circuits that allow for a rich interplay between classical and quantum compute capabilities, all within the coherence time of the computation, and will be crucial for the development of error correction and thus fault tolerant quantum computation. However, there are many technical milestones along the way that track progress before we achieve this ultimate goal. Chief among these is the ability to measure and reset a qubit in the middle of a circuit execution, which we have now enabled across the fleet of IBM Quantum systems available via the IBM Cloud.

Also Read: C2010-555: IBM Maximo Asset Management v7.6 Functional Analyst

Measurement is at the very heart of quantum computing. Although often overlooked, high-fidelity measurements allow for classical systems (including us humans) to faithfully extract information from the realm in which quantum computers operate. Measurements typically take place at the end of a quantum circuit, allowing, with repeated executions, one to gather information about the final state of a quantum system in the form of a discrete probability distribution in the computational basis. However, there are distinct computational advantages to being able to measure a qubit in the middle of a computation.

Mid-circuit measurements play two primary roles in computations. First, they can be thought of as Boolean tests for a property of a quantum state before the final measurement takes place. For example, one can ask, mid-circuit, whether a register of qubits is in the plus or minus eigenstate of an operator formed by a tensor product of Pauli operators. Such “stabilizer” measurements form a core component of quantum error correction, signaling the presence of an error to be corrected. Likewise, mid-circuit measurements can be used to validate the state of a quantum computer in the presence of noise, allowing for post-selection of the final measurement outcomes based on the success of one or more sanity checks.

Measurements performed while a computation is in flight can have some other surprising functions, too — like directly influencing the dynamics of the quantum system. If the system is initially prepared in a highly entangled state, then a judicious choice of local measurements can “steer” a computation in a desired direction. For example, we can produce a three-qubit GHZ state and transform it into a Bell-state via an x-basis measurement on one of the three qubits; this would otherwise yield a mixed state if measured in the computational basis. More complex examples include cluster state computation, where the entire computation is imprinted onto the qubit’s state via a sequence of measurements.

Resetting a qubit

Closely related to mid-circuit measurements is the ability to reset a qubit to its ground state at any point in a computation. Many critical applications, such as solving linear systems of equations, make use of auxiliary qubits as working space during a computation. A calculation requires significantly fewer qubits if, once used, we can return a qubit to the ground state with high-fidelity. With system sizes in the range of 100 qubits, space is at a premium in today’s nascent quantum systems, and on-demand reset is necessary for enabling complex applications on near-term hardware. In Figure 1, below, we highlight an example of the quality of the reset operations on IBM Quantum’s current generation of Falcon processors, on the Montreal system, by looking at the error associated with one or more reset operations applied to a random single-qubit initial state.

Figure 1: we highlight an example of the quality of the reset operations on IBM Quantum’s current generation of Falcon_r4 processors by looking at the error associated with one or more reset operations applied to a random single-qubit initial state.

Internally, these reset instructions are composed of a mid-circuit measurement followed by an x-gate conditioned on the outcome of the measurement.  These conditional reset operations therefore represent one of IBM Quantum’s first forays into dynamic quantum circuits, alongside our recent results demonstrating an implementation of an iterative phase estimation algorithm. However, while the control techniques necessary for iterative phase estimation are still a research prototype, you can use mid-circuit measurement and conditional resets, today.

We can incorporate both concepts illustrated here into simple examples. First, Figure 2 shows a circuit utilizing both mid-circuit measurements and conditional reset instructions for post-selection and qubit reuse.

Figure 2: a circuit utilizing both mid-circuit measurements and conditional reset instructions for post-selection and qubit reuse. 

This circuit first initializes all of the qubits into the ground state, and then prepares qubit 0 (q0) into an unknown state via the application of a random SU(2) unitary.  Next, it projects q0 into the x-basis with eigenvalues 0 or 1 imprinted on q1 indicating if the qubit is left in the |+> (0) or |-> (1) x-basis states. We measure q1, and store the result for later use as a flag qubit for identifying which output states correspond to each eigenvalue. Step 3 of the circuit resets the already-measured q1 to the ground state, and then generates an entangled Bell pair between the two qubits. The Bell pair is either |00>+|11> or |00>-|11> depending on if q0 is in the |+> or |-> state prior to the CNOT gate, respectively. Finally, in order to distinguish these states, we use Hadamard gates to transform the state |00>-|11> to |01>+|10> before measuring.

Figure 3 shows the outcome of executing such a circuit on the seven-qubit IBM Quantum Casablanca system, where we see that that the measurement of the flag qubit value measured before (in bold) correctly tracks the expected Bell states generated at the output. Collecting marginal counts over the flag qubit value indicates the proportion of the initial random q0 state that was in the |+> or |-> state after the projection. For the example considered here, these values are ~16 percent and ~84 percent, respectively. The dominant source of the error in the result is dephasing due to the relatively long (~4㎲) duration of measurements on current generation systems. Future processor revisions will bring faster measurements, reducing the effect of this error.

Next, we consider the computational advantages of using reset to reduce the number of qubits needed in a 12-qubit Bernstein-Vazirani problem (Fig. 3). As written, this circuit cannot be implemented directly on an IBM Quantum system, but rather requires the introduction of SWAP gates in order to satisfy the limited connectivity in systems such as our heavy-hex based Falcon and Hummingbird processors. Indeed, compiling this circuit with Qiskit yields a circuit that requires 42 CNOT gates on a heavy-hex lattice.  The fidelity of executing this compiled circuit on the IBM Quantum Kolkata system yields a disappointing 0.007; the output is essentially noise.

However, with the ability to measure and reset qubits mid-flight, we can transform any Berstein-Vazirani circuit into a circuit over just two qubits requiring no additional SWAP gates. For the previous example, the corresponding circuit is:

And execution on the same system gives a vastly improved fidelity of 0.31; a 400x improvement over the standard implementation. This highlights how, with mid-circuit measurement and reset, it is possible to write compact algorithms with markedly higher fidelity than would otherwise be possible without these dynamic circuit building blocks.

Mid-circuit measurement and conditional reset represent an important first step toward dynamic circuits — and one that you can begin implementing into your quantum circuits as we speak. We’re excited to see what our users can do with this new functionality, while we continue to expand the variety of circuits that our devices can run. We hope you’ll follow along as we implement our development roadmap; we’re working to make the power of dynamic circuits a regular part of quantum computation in just a few years.

Source: ibm.com

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