A guest article by Michael J. Biercuk, Harry Slatyer, and Michael Hush of - a toolset for combining state-of-the-art machine learning techniques with quantum algorithm design. This was an important step to build tools for developers working on quantum applications - users operating primarily at the “top of the stack”.
In parallel we’ve been building a complementary TensorFlow-based toolset working from the hardware level up - from the bottom of the stack. Our efforts have focused on improving the performance of quantum computing hardware through the integration of a set of techniques we call quantum firmware.
In this article we’ll provide an overview of the fundamental driver for this work - combating noise and error in quantum computers - and describe how the team at Q-CTRL uses TensorFlow to efficiently characterize and suppress the impact of noise and imperfections in quantum hardware. These are key challenges in the global effort to make quantum computers useful.
Quantum firmware describes a set of protocols whose purpose is to deliver quantum hardware with augmented performance to higher levels of abstraction in the quantum computing stack. The choice of the term firmware reflects the fact that the relevant routines are usually software-defined but embedded proximal to the physical layer and effectively invisible to higher layers of abstraction.
Quantum computing hardware generally relies on a form of precisely engineered light-matter interaction in order to enact quantum logic operations. These operations in a sense constitute the native machine language for a quantum computer; a timed pulse of microwaves on resonance with a superconducting qubit can translate to an effective bit-flip operation while another pulse may implement a conditional logic operation between a pair of qubits. An appropriate composition of these electromagnetic signals then implements the target quantum algorithm.
Quantum firmware determines how the physical hardware should be manipulated, redefining the hardware machine language in a way that improves stability against decoherence. Key to this process is the calculation of noise-robust operations using information gleaned from the hardware itself.
Building in TensorFlow was essential to moving beyond “home-built’’ code to commercial-grade products for Q-CTRL. Underpinning these techniques (formally coming from the field of quantum control) are tools allowing us to perform complex gradient-based optimizations. We express all optimization problems as data flow graphs, which describe how optimization variables (variables that can be tuned by the optimizer) are transformed into the cost function (the objective that the optimizer attempts to minimize). We combine custom convenience functions with access to TensorFlow primitives in order to efficiently perform optimizations as used in many different parts of our workflow. And critically, and contains many complex dependencies.
For example, consider the case of defining a numerically optimized error-robust quantum bit flip used to manipulate a qubit - the analog of a classical NOT gate. As mentioned above, in a superconducting qubit this is achieved using a pulse of microwaves. We have the freedom to “shape” various aspects of the envelope of the pulse in order to enact the same mathematical transformation in a way that exhibits robustness against common noise sources, such as fluctuations in the strength or frequency of the microwaves.
To do this we first define the data flow graph used to optimize the manipulation of this qubit - it includes objects that describe available “knobs” to adjust, the sources of noise, and the target operation (here a Hadamard gate).
| has revealed order-of-magnitude benefits in time to solution relative to the best available alternative architectures. The capabilities enabled by this toolkit span the space of tasks required to stabilize quantum computing hardware and reduce errors at the lowest layer of the quantum computing stack. And importantly they’re experimentally verified on real quantum computing hardware; quantum firmware has been and suggests that we’re unlikely to see Shor deployed at a useful scale until 2039. Today, small-scale machines with a couple of dozen interacting qubits exist in labs around the world, built from superconducting circuits, individual trapped atoms, or similarly exotic materials. The problem is that these early machines are just too small and too fragile to solve problems relevant to factoring. To factor a number sufficiently large to be relevant in cryptography, one would need a system composed of thousands of qubits capable of handling trillions of operations each. This is nothing for a conventional machine where hardware can run for a billion years at a billion operations per second and never be likely to suffer a fault. But as we’ve seen it’s quite a different story for quantum computers. These limits have driven the emergence of a new class of applications in materials science and . This class of problem can often be cast as optimizations where input into a classical machine learning algorithm comes from a small quantum computation, or where data is represented in the quantum domain and a learning procedure implemented. TensorFlow Quantum provides an exciting toolset for developers seeking new and improved ways to exploit the small quantum computers existing now and in the near future. Still, even those small machines don’t perform particularly well. Q-CTRL’s quantum firmware enables users to extract maximum performance from hardware. Thus we see that TensorFlow has a critical role to play across the emerging quantum computing software stack - from quantum firmware through to algorithms for quantum machine learning. Resources if you’d like to learn moreWe appreciate that members of the TensorFlow community may have varying levels of familiarity with quantum computing, and that this overview was only a starting point. To help readers interested in learning more about quantum computing we’re happy to provide a few resources:
Vollständiger Original-Bericht Ausführliche Details, Code-Beispiele & Hersteller-Stellungnahme auf blog.tensorflow.org. Wie bewertest du diesen Beitrag? 1 Klick Feedback Teilen mit Netzwerk & Team: Hat Ihnen dieser Tipp / Anleitung geholfen? Community-Analysen & Experten-Meinungen 0Verfasse deine eigene Analyse, teile Workarounds oder diskutiere diesen Vorfall im Blog. Noch keine Community-Analyse verfasst. Markiere einen Textabschnitt oder klicke oben auf „ Eigene Analyse verfassen“! Community Pulse: Relevanz-Einschätzung 1 Klick Experten-Votum 🔴 Akute Relevanz 0% 🟡 In Evaluierung 0% 🟢 Keine Auswirkung 0% Spannende Innovation 0% Verwandte Story-Cluster & Quellen (Vektor-KI) Tipp: Mit Pfeiltasten [ ← ] und [ → ] blättern 
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