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Register a new userExtending Python for Quantum-Classical Computing via Quantum Just-in-Time Compilation
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Python is a popular programming language known for its flexibility, usability, readability, and focus on developer productivity. The quantum software community has adopted Python on a number of large-scale efforts due to these characteristics, as well as the remote nature of near-term quantum processors. The use of Python has enabled quick prototyping for quantum code that directly benefits pertinent research and development efforts in quantum scientific computing. However, this rapid prototyping ability comes at the cost of future performant integration for tightly-coupled CPU-QPU architectures with fast-feedback. Here we present a language extension to Python that enables heterogeneous quantum-classical computing via a robust C++ infrastructure for quantum just-in-time (QJIT) compilation. Our work builds off the QCOR C++ language extension and compiler infrastructure to enable a single-source, quantum hardware-agnostic approach to quantum-classical computing that retains the performance required for tightly coupled CPU-QPU compute models. We detail this Pythonic extension, its programming model and underlying software architecture, and provide a robust set of examples to demonstrate the utility of our approach.
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To date, blind quantum computing demonstrations require clients to have weak quantum devices. Here we implement a proof-of-principle experiment for completely classical clients. Via classically interacting with two quantum servers that share entanglement, the client accomplishes the task of having the number 15 factorized by servers who are denied information about the computation itself. This concealment is accompanied by a verification protocol that tests servers honesty and correctness. Our demonstration shows the feasibility of completely classical clients and thus is a key milestone towards secure cloud quantum computing.
We propose a new point of view regarding the problem of time in quantum mechanics, based on the idea of replacing the usual time operator $mathbf{T}$ with a suitable real-valued function $T$ on the space of physical states. The proper characterization of the function $T$ relies on a particular relation with the dynamical evolution of the system rather than with the infinitesimal generator of the dynamics (Hamiltonian). We first consider the case of classical Hamiltonian mechanics, where observables are functions on phase space and the tools of differential geometry can be applied. The idea is then extended to the case of the unitary evolution of pure states of finite-level quantum systems by means of the geometric formulation of quantum mechanics. It is found that $T$ is a function on the space of pure states which is not associated to any self-adjoint operator. The link between $T$ and the dynamical evolution is interpreted as defining a simultaneity relation for the states of the system with respect to the dynamical evolution itself. It turns out that different dynamical evolutions lead to different notions of simultaneity, i.e., the notion of simultaneity is a dynamical notion.
Computational methods are the most effective tools we have besides scientific experiments to explore the properties of complex biological systems. Progress is slowing because digital silicon computers have reached their limits in terms of speed. Other types of computation using radically different architectures, including neuromorphic and quantum, promise breakthroughs in both speed and efficiency. Quantum computing exploits the coherence and superposition properties of quantum systems to explore many possible computational paths in parallel. This provides a fundamentally more efficient route to solving some types of computational problems, including several of relevance to biological simulations. In particular, optimisation problems, both convex and non-convex, feature in many biological models, including protein folding and molecular dynamics. Early quantum computers will be small, reminiscent of the early days of digital silicon computing. Understanding how to exploit the first generation of quantum hardware is crucial for making progress in both biological simulation and the development of the next generations of quantum computers. This review outlines the current state-of-the-art and future prospects for quantum computing, and provides some indications of how and where to apply it to speed up bottlenecks in biological simulation.
We experimentally demonstrate over two orders of magnitude increase in the coherence time of nitrogen vacancy centres in diamond by implementing decoupling techniques. We show that equal pulse spacing decoupling performs just as well as non-periodic Uhrig decoupling and has the additional benefit that it allows us to take advantage of revivals in the echo (due to the coherent nature of the bath) to explore the longest coherence times. At short times, we can extend the coherence of particular quantum states out from T_2*=2.7 us out to an effective T_2 > 340 us. For preserving arbitrary states we show the experimental importance of using pulse sequences, that through judicious choice of the phase of the pulses, compensate the imperfections of individual pulses for all input states. At longer times we use these compensated sequences to enhance the echo revivals and show a coherence time of over 1.6 ms in ultra-pure natural abundance 13C diamond.
An application of quantum cloning to optimally interface a quantum system with a classical observer is presented, in particular we describe a procedure to perform a minimal disturbance measurement on a single qubit by adopting a 1->2 cloning machine followed by a generalized measurement on a single clone and the anti-clone or on the two clones. Such scheme has been applied to enhance the transmission fidelity over a lossy quantum channel.
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