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arXiv:quant-ph/0111120

Quantum-Statistical Computation

Giuseppe Castagnoli, David Finkelstein

Published Nov 22, 2001• 6 citations
arXivHTMLPDF

Abstract

Systems of spin 1, such as triplet pairs of spin-1/2 fermions (like orthohydrogen nuclei) make useful three-terminal elements for quantum computation, and when interconnected by qubit equality relations are universal for quantum computation. This is an instance of quantum-statistical computation: some of the logical relations of the problem are satisfied identically in virtue of quantum statistics, which takes no time. We show heuristically that quantum-statistical ground-mode computation is substantially faster than pure ground-mode computation when the ground mode is reached by annealing.

DOI
10.48550/arxiv.quant-ph/0111120

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Systems of spin 1, such as triplet pairs of spin-1/2 fermions (like orthohydrogen nuclei) make useful three-terminal elements for quantum computation, and when interconnected by qubit equality relations are universal for quantum computation. This is an instance of quantum-statistical computation: some of the logical relations of the problem are satisfied identically in virtue of quantum statistics, which takes no time. We show heuristically that quantum-statistical ground-mode computation is substantially faster than pure ground-mode computation when the ground mode is reached by annealing.
We develop a computation model for solving Boolean networks by implementing wires through quantum ground-mode computation and gates through identities following from angular momentum algebra and statistics. Gates are represented by three-dimensional (triplet) symmetries due to particle indistinguishability and are identically satisfied throughout computation being constants of the motion. The relaxation of the wires yields the network solutions. Such gates cost no computation time, which is comparable with that of an easier Boolean network where all the gate constraints implemented as constants of the motion are removed. This model computation is robust with respect to decoherence and yields a generalized quantum speed-up for all NP problems.
Quantum computation offers the potential for solving specific problems exponentially faster than the best classical machines, by virtue of its non-classical dynamics and information representation. Quantum mechanical bits (called 'qubits') can be both 0 and 1 at the same time, in which case the qubit is said to be in a superposition state. A quantum gate with input 0 or 1 gives the same output as a classical gate, but if the input is 0 and 1 simultaneously, the quantum gate performs the two corresponding operations simultaneously. Similarly, a 2-qubit gate with an input state prepared as a superposition of the 4 combinations 00, 01, 10 and 11, performs four simultaneous computations. In general, an N-qubit gate could perform 2/sup N/ computations at the same time. So as we increase the size of the system, the computational capacity of the apparatus goes up exponentially, compared to linearly for a classical computer. Using commercial NMR-spectrometers, simple quantum circuits are experimentally implemented consisting of multiple cascaded logic gates.
We study the performance of quantum annealing for systems with ground-state degeneracy by directly solving the Schrödinger equation for small systems and quantum Monte Carlo simulations for larger systems. The results indicate that naive quantum annealing using a transverse field may not be well suited to identify all degenerate ground-state configurations, although the value of the ground-state energy is often efficiently estimated. An introduction of quantum transitions to all states with equal weights is shown to greatly improve the situation, but with a sacrifice in annealing time. We also clarify the relation between the spin configurations in degenerate ground states and the probabilities that those states are obtained by quantum annealing. The strengths and weaknesses of quantum annealing for problems with degenerate ground states are discussed in comparison with classical simulated annealing.
Tailoring many-body interactions among a proper quantum system endows it with computing ability by means of static quantum computation in the sense that some of the physical degrees of freedom can be used to store binary information and the corresponding binary variables satisfy some given logic relations if and only if the system is in the ground state. Two theorems are proved showing that the universal static quantum computer can encode the solutions for any P and NP problem into its ground state using only polynomial number (in the problem input size) of logic gates. The second step is to read out the solutions by relaxing the system. The time complexity is relevant when one tries to read out the solution by relaxing the system, therefore our model of static quantum computation provides a new connection between the computational complexity and the dynamics of a complex system.
Quantum particle statistics fundamentally controls the way particles interact and plays an essential role in determining the properties of the system at low temperature. Here we study how the quantum statistics affects the computational power of quantum annealing. We propose an annealing Hamiltonian describing quantum particles moving on a square lattice and compare the computational performances of the atomic quantum annealers between two statistically different components: spinless fermions and hard-core bosons. In addition, we take an Ising quantum annealer driven by traditional transverse-field quantum fluctuations as a baseline. The potential of our quantum annealers to solve combinatorial optimization problems is demonstrated on random 3-regular graph partitioning. We find that the bosonic quantum annealer outperforms the fermionic case. The superior performance of the bosonic quantum annealer is attributed to larger excitation gaps and the consequent smoother adiabatic transformation of its instantaneous quantum ground states. Along our annealing schedule, the bosonic quantum annealer is less affected by the glass order and explores the Hilbert space more efficiently. Our theoretical finding could shed light on constructing atomic quantum annealers using Rydberg atoms in optical lattices.
We introduce Apollo, a 10000 node p-qubit neuromorphic processor fabricated in 16 nm mixed signal CMOS and operating fully at room temperature with a typical analog core power envelope of about 0.5 W. Its fundamental element, the p-qubit, is a bistable stochastic unit whose continuous time state fluctuations are driven by integrated quantum entropy units that inject true quantum derived randomness. This enables ultrafast stochastic transitions at low energy while preserving a classical state representation. Apollo combines these p-qubits with a high degree Hyperion 256 interconnect topology, allowing efficient embedding of dense Ising and QUBO problems with substantially reduced minor embedding overhead compared with sparse annealing platforms. We show that, through the Suzuki Trotter correspondence, the equilibrium statistics and annealing dynamics of the p-qubit network reproduce key properties of transverse field quantum annealing without cryogenic cooling, long lived coherence, or microwave control. Beyond device level validation, Apollo is evaluated on a three dimensional spin glass benchmark previously used to study quantum advantage in superconducting annealers. Across 300 disorder realizations, Apollo reaches substantially lower ground state energies than reported cryogenic quantum annealing hardware, while remaining distinct from classical simulated annealing and simulated quantum annealing. A 350 nm release candidate device experimentally validates the core p-qubit dynamics, thermodynamic sampling correctness, and continuous time annealing behavior. These results establish Apollo as a room temperature, industrially scalable platform for quantum driven energy based optimization, probabilistic inference, generative modeling, and hybrid classical quantum workflows.