16,227 research outputs found
Quantum Simulations of Classical Random Walks and Undirected Graph Connectivity
AbstractWhile it is straightforward to simulate a very general class of random processes space-efficiently by non-unitary quantum computations (e.g., quantum computations that allow intermediate measurements to occur), it is not currently known to what extent restricting quantum computations to be unitary affects the space required for such simulations. This paper presents a method by which a limited class of random processes—random walks on undirected graphs—can be simulated by unitary quantum computations in a space-efficient (and time-efficient) manner. By means of such simulations, it is demonstrated that the undirected graph connectivity problem for regular graphs can be solved by one-sided error quantum Turing machines that run in logspace and require a single measurement at the end of their computations. It follows that symmetric logspace is contained in a quantum analogue of randomized logspace that disallows intermediate measurements
Time and Space Bounds for Reversible Simulation
We prove a general upper bound on the tradeoff between time and space that
suffices for the reversible simulation of irreversible computation. Previously,
only simulations using exponential time or quadratic space were known.
The tradeoff shows for the first time that we can simultaneously achieve
subexponential time and subquadratic space.
The boundary values are the exponential time with hardly any extra space
required by the Lange-McKenzie-Tapp method and the ()th power time with
square space required by the Bennett method. We also give the first general
lower bound on the extra storage space required by general reversible
simulation. This lower bound is optimal in that it is achieved by some
reversible simulations.Comment: 11 pages LaTeX, Proc ICALP 2001, Lecture Notes in Computer Science,
Vol xxx Springer-Verlag, Berlin, 200
Quantum Analogue Computing
We briefly review what a quantum computer is, what it promises to do for us,
and why it is so hard to build one. Among the first applications anticipated to
bear fruit is quantum simulation of quantum systems. While most quantum
computation is an extension of classical digital computation, quantum
simulation differs fundamentally in how the data is encoded in the quantum
computer. To perform a quantum simulation, the Hilbert space of the system to
be simulated is mapped directly onto the Hilbert space of the (logical) qubits
in the quantum computer. This type of direct correspondence is how data is
encoded in a classical analogue computer. There is no binary encoding, and
increasing precision becomes exponentially costly: an extra bit of precision
doubles the size of the computer. This has important consequences for both the
precision and error correction requirements of quantum simulation, and
significant open questions remain about its practicality. It also means that
the quantum version of analogue computers, continuous variable quantum
computers (CVQC) becomes an equally efficient architecture for quantum
simulation. Lessons from past use of classical analogue computers can help us
to build better quantum simulators in future.Comment: 10 pages, to appear in the Visions 2010 issue of Phil. Trans. Roy.
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