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arXiv:2411.03110

Multiple-basis representation of quantum states

Adrián Pérez-Salinas, Patrick Emonts, Jordi Tura, Vedran Dunjko

Published Nov 5, 2024• 0 citations
arXivHTMLPDF

Abstract

Classical simulation of quantum physics is a central approach to investigating physical phenomena. Quantum computers enhance computational capabilities beyond those of classical resources, but it remains unclear to what extent existing limited quantum computers can contribute to this enhancement. In this work, we explore a new hybrid, efficient quantum-classical representation of quantum states, the multiple-basis representation. This representation consists of a linear combination of states that are sparse in some given and different bases, specified by quantum circuits. Such representation is particularly appealing when considering depth-limited quantum circuits within reach of current hardware. We analyze the expressivity of multiple-basis representation states depending on the classical simulability of their quantum circuits. In particular, we show that multiple-basis representation states include, but are not restricted to, both matrix-product states and stabilizer states. Furthermore, we find cases in which this representation can be used, namely approximation of ground states, simulation of deeper computations by specifying bases with shallow circuits, and a tomographical protocol to describe states as multiple-basis representations. We envision this work to open the path of simultaneous use of several hardware-friendly bases, a natural description of hybrid computational methods accessible for near-term hardware.

DOI
10.48550/arxiv.2411.03110

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Classical simulation of quantum physics is a central approach to investigating physical phenomena. Quantum computers enhance computational capabilities beyond those of classical resources, but it remains unclear to what extent existing limited quantum computers can contribute to this enhancement. In this work, we explore a new hybrid, efficient quantum-classical representation of quantum states, the multiple-basis representation. This representation consists of a linear combination of states that are sparse in some given and different bases, specified by quantum circuits. Such representation is particularly appealing when considering depth-limited quantum circuits within reach of current hardware. We analyze the expressivity of multiple-basis representation states depending on the classical simulability of their quantum circuits. In particular, we show that multiple-basis representation states include, but are not restricted to, both matrix-product states and stabilizer states. Furthermore, we find cases in which this representation can be used, namely approximation of ground states, simulation of deeper computations by specifying bases with shallow circuits, and a tomographical protocol to describe states as multiple-basis representations. We envision this work to open the path of simultaneous use of several hardware-friendly bases, a natural description of hybrid computational methods accessible for near-term hardware.
Simulation of quantum information processing remains a major challenge with important applications in quantum computer science and engineering. Generic quantum-circuit simulation appears intractable for conventional computers and may be unnecessary because useful quantum circuits exhibit significant structure that can be exploited during simulation. For example, Gottesman and Knill identified an important subclass, called stabilizer circuits, which can be simulated efficiently using the Heisenberg representation for quantum computers. Stabilizer circuits are exclusively composed of stabilizer gates -- Hadamard, Phase and CNOT -- followed by one-qubit measurements in the computational basis. Such circuits are applied to a computational-basis state and produce so-called stabilizer states. Aaronson and Gottesman generalized stabilizer-circuit simulation to additionally handle a small number of non-stabilizer gates. We design new, more efficient data structures and algorithms for such beyond-stabilizer simulation using superpositions of stabilizer states. One such data structure, a stabilizer frame, offers more compact storage than previous approaches but require additional algorithms to maintain the global phases of each state in the superposition. To explore the advantages and limitations of our technique, we analyze the geometric structure of stabilizer states and their embedding in Hilbert space. Our analysis includes results on the computational geometry of stabilizer states such as efficient algorithms for computing distances, angles and volumes between them. The main advantages of using stabilizer-state superpositions to simulate quantum circuits are: (i) stabilizer subcircuits are simulated with high efficiency, (ii) superpositions can be restructured and compressed on the fly during simulation to reduce resource requirements, and (iii) operations performed on such superpositions lend themselves to distributed or asynchronous processing. Our software implementation, called Quipu, simulates certain quantum arithmetic circuits (e.g., reversible ripple-carry adders) and quantum Fourier transform circuits in polynomial time and space for specific input states. On such instances, known linear-algebraic simulation techniques, such as the (state-of-the-art) BDD-based simulator QuIDDPro, take exponential time. We simulate quantum fault-tolerant circuits using Quipu, and the results indicate that our stabilizer-based technique empirically outperforms QuIDDPro in all cases. While previous structure-aware simulations of quantum circuits were difficult to parallellize, we demonstrate a parallel version of Quipu that achieves a nontrivial speedup.
Abstract Determining the ground and excited state properties of materials is considered one of the most promising applications of quantum computers. On near-term hardware, the limiting constraint on such simulations is the requisite circuit depths and qubit numbers, which currently lie well beyond near-term capabilities. Here we develop a quantum algorithm which reduces the estimated cost of material simulations. For example, we obtain a circuit depth improvement by up to 6 orders of magnitude for a Trotter layer of time-dynamics simulation in the transition-metal oxide SrVO3compared with the best previous quantum algorithms. We achieve this by introducing a collection of connected techniques, including highly localised and physically compact representations of materials Hamiltonians in the Wannier basis, a hybrid fermion-to-qubit mapping, and an efficient circuit compiler. Combined together, these methods leverage locality of materials Hamiltonians and result in a design that generates quantum circuits with depth independent of the system’s size. Although the requisite resources for the quantum simulation of materials are still beyond current hardware, our results show that realistic simulation of specific properties may be feasible without necessarily requiring fully scalable, fault-tolerant quantum computers, providing quantum algorithm design incorporates deeper understanding of the target materials and applications.
Dynamical simulation of quantum many-body systems is a central task in quantum chemistry and requires efficient wavefunction representations. Although tensor-network and neural-network quantum states have achieved considerable success in ground-state calculations, entanglement growth hinders their application to quantum dynamics. Quantum computing may offer a route to quantum advantage, but algorithms such as Trotterization generally require fault-tolerant quantum computers, whereas near-term hardware supports only limited circuit sizes. Variational quantum simulation (VQS) instead represents the evolving state with a single shallow, fixed-size parameterized quantum circuit (PQC). Here, we introduce nonorthogonal variational quantum simulation (NOVQS), which applies linear combinations of parameterized quantum states to real- and imaginary-time evolutions. We design a shallow, hardware-friendly ansatz tailored to second-quantized electronic-structure Hamiltonians, together with resource-efficient protocols for measuring the matrices and vectors in the parameter equations of motion. Error analysis and resource estimation are also provided. Numerical simulations of hydrogen chains and the nitrogen molecule demonstrate that a collection of shallow, or even single-layer, PQCs can match or outperform a much deeper PQC in VQS. In particular, we identify a trade-off between circuit number and depth. NOVQS therefore provides a flexible route to enhancing wavefunction expressivity under circuit-depth constraints, making it a promising framework for quantum dynamics simulations on near-term quantum processors.
This paper explores the potential of utilizing entangled multi-body states for enhanced quantum simulation. Traditional quantum simulation methods face limitations when simulating complex quantum systems, primarily due to their inability to effectively represent multi-particle correlations. We propose a novel approach leveraging the inherent properties of entangled states to achieve a more accurate and efficient simulation of these systems. Specifically, we examine the construction of quantum computers incorporating multi-body entangled states and their application to simulating quantum field theories, a domain where complex many-body interactions are prevalent. The core idea centers on exploiting the correlations encoded within entangled states to reduce the computational complexity associated with simulating high-dimensional quantum systems. We present a theoretical framework outlining the key challenges and potential strategies for realizing this approach, focusing on the design of appropriate quantum circuits and the optimization of entanglement protocols. The ultimate goal is to demonstrate a pathway towards simulating intricate quantum phenomena that are currently intractable using conventional simulation techniques. The simulation method is based on mapping the problem to a quantum computer. The quantum computer performs the simulation based on the state of the qubits. The final result is the answer to the original problem. The key mathematical tool is the quantum circuit. The quantum circuit is a series of quantum gates applied to qubits. The quantum gates are unitary transformations. The unitary transformations are represented by matrices. The matrix is a square matrix. The matrix is a linear transformation. The linear transformation is a transformation that preserves linear relationships. The eigenvalues of the matrix are the solutions to the equation det(A - λI) = 0, where A is the matrix and λ is the eigenvalue and I is the identity matrix. The eigenvectors of the matrix are the vectors that are transformed to themselves by the matrix. The eigenvectors are the solutions to the equation (A - λI)v = 0, where A is the matrix, λ is the eigenvalue and v is the eigenvector. The eigenvalues and eigenvectors are important concepts in linear algebra.
Tensor network theory and quantum simulation are, respectively, the key classical and quantum computing methods in understanding quantum many-body physics. Here, we introduce the framework of hybrid tensor networks with building blocks consisting of measurable quantum states and classically contractable tensors, inheriting both their distinct features in efficient representation of many-body wave functions. With the example of hybrid tree tensor networks, we demonstrate efficient quantum simulation using a quantum computer whose size is significantly smaller than the one of the target system. We numerically benchmark our method for finding the ground state of 1D and 2D spin systems of up to 8×8 and 9×8 qubits with operations only acting on 8+1 and 9+1 qubits, respectively. Our approach sheds light on simulation of large practical problems with intermediate-scale quantum computers, with potential applications in chemistry, quantum many-body physics, quantum field theory, and quantum gravity thought experiments.