But wouldn’t it be cool to be able to go further, to actually look INSIDE a quantum computer, with large numbers of qubits all interacting and computing, and catch the quantum mechanics in the act?
* This is the workings of a quantum processor. It is programmable – it actually solves problems, looks similar to the integrated circuits inside your laptop, and you can program it using Python
* They can use those quantum effects to compute
The unit cell mentioned above is operated in the same way as it would be during a normal computation – running what is known as a quantum annealing algorithm. The difference is that at a certain point during the computation, the usually slow, careful annealing of the qubits is suddenly interrupted by a very fast signal. This signal causes the unit cell to ‘freeze’ in whatever state it was in at the time. If you repeat the computation lots of times, but each time apply your ‘freezing’ signal at a slightly different moment during the quantum computation, you can build up a series of ‘snapshots’, like stills on a movie reel. D-Wave scientists compiled all these snapshots to reveal exactly what is happening during the quantum computation.
Nature - Quantum annealing with manufactured spins
A theoretical model of the unit cell was set up, based on the predictions of quantum physics, and the model fits very well indeed. Even more interesting, a second model was set up, which captured how CLASSICAL physics predicts the processor should behave. The results were striking – the classical model wasn’t even close. There’s no way these results can be explained using classical physics.
Many interesting but practically intractable problems can be reduced to that of finding the ground state of a system of interacting spins; however, finding such a ground state remains computationally difficult. It is believed that the ground state of some naturally occurring spin systems can be effectively attained through a process called quantum annealing. If it could be harnessed, quantum annealing might improve on known methods for solving certain types of problem. However, physical investigation of quantum annealing has been largely confined to microscopic spins in condensed-matter systems. Here we use quantum annealing to find the ground state of an artificial Ising spin system comprising an array of eight superconducting flux quantum bits with programmable spin–spin couplings. We observe a clear signature of quantum annealing, distinguishable from classical thermal annealing through the temperature dependence of the time at which the system dynamics freezes. Our implementation can be configured in situ to realize a wide variety of different spin networks, each of which can be monitored as it moves towards a low-energy configuration. This programmable artificial spin network bridges the gap between the theoretical study of ideal isolated spin networks and the experimental investigation of bulk magnetic samples. Moreover, with an increased number of spins, such a system may provide a practical physical means to implement a quantum algorithm, possibly allowing more-effective approaches to solving certain classes of hard combinatorial optimization problems.
Quantum computers could outperform a classical computer at some tasks – at least in principle – thanks to two key quantum properties. These are that a qubit can be in a superposition of two or more quantum states and that two or more qubits can be entangled. But the big challenge for D-Wave – and for everyone else trying to build a quantum computer – is how to create qubits and computing processes that are resistant to the destructive effects of heat and noise.
Using flux qubits is attractive in that quest because they are macroscopic structures that can be created using semiconductor-manufacturing processes and can be controlled using applied currents and voltages. A downside is that they have a multitude of quantum states, not just two. The task for D-Wave is how to place each qubit in a well-defined and useful quantum state without it being corrupted by heat or noise – essentially the analogue of writing data to a classical computer.
The method chosen by the firm to do this is called "quantum annealing" – and now D-Wave has shown that it can use this technique to place eight coupled qubits into the appropriate lowest energy state. The researchers began with eight superconducting flux qubits within one of D-Wave’s integrated circuits. These contain 128 flux qubits arranged into 16 units of eight. The system is then cooled to a temperature of 10 mK, which puts each qubit into a superposition of two quantum states with identical energy.
This superposition is not, however, particularly useful and the next step is to manipulate each qubit into a pure spin-up or spin-down state. Each loop is broken by a structure containing two Josephson junctions and a magnetic coil. When a current is applied to the coil, an energy barrier rises between the spin-up and spin-down states. In a classical system, the loop would be forced into either the up or down state and could hop between states by absorbing heat from the surroundings. A qubit however, remains in a superposition of up and down as long as the barrier rises slowly enough.
Each qubit has a second magnetic coil, which is used to "tip" the qubit into the desired pure state. If the field is applied in the up direction, for example, the energy of the spin-up state drops below that of the spin-down state, thereby making it more likely that the qubit will become pure spin-up. The problem facing D-Wave is that this transition occurs both by quantum-mechanical tunnelling and by absorbing heat (thermal excitation). Thermal excitation destroys the quantum nature of the qubit, and so must be avoided during quantum annealing.
The two processes can be distinguished by raising the barrier until both tunnelling and heat-driven transitions stop (the qubit "freezes") – and then repeating this process at different temperatures. The research team found that below about 45 mK, freezing is affected primarily by barrier height and not temperature, which is what is expected if annealing occurs by tunnelling alone.
The team then showed that it could anneal a unit of eight qubits. The researchers did this by adjusting the interactions between the qubits to simulate a 1D chain of magnets in which each qubit wants to point in the same direction as its two neighbours. The qubit at the right-hand end of the chain is set in the up direction and the qubit at the left-hand end in the down direction. The six qubits in the middle are then allowed to orient their spins according to that of their neighbours. The result is a "frustrated" ferromagnetic arrangement in which two neighbours must have opposing spins.
Finally, the qubits are all tilted in the same direction while the barrier is raised. This should result in the system moving towards one specific arrangement of frustrated spins – the ground state. Again, below about 45 mK, the system found its way to the ground state in a manner consistent with the spins flipping because of quantum-mechanical tunnelling, not thermal activation. "We're very excited to see the remarkable agreement between what quantum mechanics predicts and what we see in these circuits," says D-Wave’s Mark Johnson, who was lead scientist on the project.
Finding the ground state of an eight-spin system is a simple quantum calculation and therefore the D-Wave team has shown that its combination of hardware and annealing process is capable of the job.
"Important" first step
"This is the first time that the D-Wave system has been shown to exhibit quantum mechanical behaviour," says William Oliver of the Massachusetts Institute of Technology, who was not involved in the research. Oliver told physicsworld.com that when combined with D-Wave’s ability to control precisely important parameters of the qubits, this latest work is "a technical achievement and an important first step".
Looking beyond quantum computing, David Feder of the University of Calgary also sees the system as an effective quantum simulation of how electron spins interact in magnetic materials.
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