Integrated Optics Theory And Technology – This is absolutely the wrong time to review the state of photonic quantum computing—right now, the field is moving so fast that anything I say here now will be outdated in a few years. On the other hand, now is the perfect time to review it because so much has happened in the past few years that it’s important to look at where the field is today and where it’s going.
On the 20th anniversary of my book Mind at Light Speed (Free Press, 2001), this blog is the third in a series looking at the progress of three generations of light cars over the last 20 years (see my previous blogs). in the future of photonic internet and all-optical computing). This third and final update looks at the progress of the third generation of light machines: the optical quantum generation. Of the three generations, it changes the fastest.
Integrated Optics Theory And Technology
Quantum computing is almost here… and it will happen at room temperature, using light, in photonic integrated circuits! Quantum computing with linear optics
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Twenty years ago, in 2001, Emmanuel Neal and Raymond Laflamme of Los Alamos National Laboratory, together with Gerald Mulburn of the University of Queensland in Australia, published a groundbreaking theoretical paper (known as KLM).
In Quantum computing with linear optics: “A scheme for efficient quantum computing with linear optics” [1]. Until then, it was believed that a quantum computer—if it were to have the properties of a universal Turing machine—must have at least some nonlinear interaction between the qubits in the quantum gate. For example, an example of a two-qubit gate is the controlled NE or CNOT gate shown in Figure 1. With truth table and matrix of equivalent units. It is clear that one object is controlling the other, telling it what to do.
A quantum CNOT gate is interesting when there is a quantum overlap on the control line, then the two outputs are entangled.
Entanglement is a unique process that is unique to quantum systems and has no classical counterpart. Nor does it have a simple intuitive explanation. By any common logic, if the control line goes through the gate unchanged, then nothing interesting should happen on the output control line. But it isn’t. The input control line was a separate state. If any measurement was made, it will display 1 or 0 with equal probability. But once out of CNOT, the signal is somehow perfectly matched to any value on the signal output line. If a signal is measured, the measurement process pulls the state of the control output to a value equal to the measured signal. The result of the control line is 100% sure, even if nothing has been done to it! This generation of entanglement is one of the reasons why CNOTs are often the gate of choice for designing quantum circuits to realize interesting quantum algorithms.
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However, the optical implementation of CNOT is a problem, because light beams and photons do not really like to interact with each other. This is also a problem with all classic optical PCs (see my previous blog). There are ways for light to interact with light, such as nonlinear optical materials. And in quantum optics, a single atom in an optical cavity can interact with individual photons in such a way that it can act as a CNOT or coupled gate. However, the efficiency is very low and the implementation costs are very high, making it difficult or impossible to scale such systems to all the networks needed to build a universal quantum computer.
Therefore, when KLM announced its linear optics quantum computing idea, it changed the way people thought about optical quantum computing. A universal optical quantum computer can be built using only light sources, beamsplitters and photon detectors.
One way in which KLM is used is to avoid the need for direct nonlinear two-photon interactions.
. They fire off a set of photons – signal photons and auxiliary (test) photons – through their linear optical system and detect (that is, theoretically… the paper is only a theoretical proposal) auxiliary photons. If these photons are not found where they are needed, that iteration of the calculation is discarded and retried until the photons are where they are needed. When auxiliary results occur, this trigger is selected because the position of the signal is known to have changed. At this point, the signal photons are still being measured, so they are in quantum superpositions that are useful for quantum computing. After sampling, entanglement and scale collapse are used to transfer the signal photons into the desired quantum states. The sample provides an effective nonlinearity due to the degeneracy of the trapped state wave function. Of course, the downside of this approach is that many iterations are eliminated – making the computation non-deterministic.
Integrated Optics: Theory And Technology
KLM could get around the uncertainty by using as many extra photons as possible, but this would come at the cost of increased size and deployment cost, so their plan was not immediately viable. But the important thing was that he introduced the idea of linear quantum computing. (Milburn and his collaborators have my vote for a future Nobel Prize for this.) Once the idea was published, others refined it, perfected it, and found clever ways to make it more efficient and scalable. Many of these ideas were based on a technology that developed alongside quantum computing: photonic integrated circuits (PICs).
Never underestimate the power of silicon. The time, energy, and resources that are currently invested in silicon device manufacturing are so astronomical that almost nothing in this world can replace it as the dominant technology of the present and the future. So when a photon can do something better than an electron, you can assume that the photon will be placed on a silicon chip, a photonic integrated circuit (PIC).
The dream of integrated optics (the optical analogue of integrated electronics) has been alive for decades, with waveguides replacing wires and interferometers replacing transistors, all miniaturized and fabricated with thousands of silicon wafers. The advantages of PIC are obvious, but the development took a long time. When I was a postdoctoral fellow at Bell Labs in the late 1980s, everyone was talking about PICs, but they had terrible manufacturing challenges and terrible dumping losses. Fortunately, these are merely technical problems, unbounded by any fundamental law of physics, and time (and legions of researchers) have erased them.
One of the driving forces behind the maturation of PIC technology is photonic fiber optic communications (as discussed in a previous blog). Photons are the clear winners in long distance communication. In this sense, photonic information technology is closer to silicon – photons are no less likely to be replaced by future technology than silicon. Therefore, it made sense to seamlessly integrate optical fibers carrying photonic chips that direct communication and information to transfer photons to silicon chips using a full range of silicon fiber sources. True, photonic chips are not yet fully optical. They still use electronics to drive optical devices on a chip, but this photonic niche has provided the driving force behind PIC manufacturing advances.
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Figure 2 Schematic of a silicon photonic integrated circuit (PIC). Waveguides can be silicon dioxide or nitride deposited on a silicon chip. From the Comsol website.
A side effect of the improved PIC construction is a slight loss of light. In telecommunications, this loss is not so significant because the systems use OEO regeneration. But lower losses are always better, and PICs can now store almost every photon on the chip—exactly what a quantum PIC needs. In a quantum photonic circuit, each photon is valuable and informative and must be protected. A new PIC product can do this. In addition, telecommunication light switches are designed with interferometers integrated on a chip. It turns out that interferometers at the single-photon level are unitary quantum gates that can be used to build universal photonic quantum computers. So the same technology and control that was used for telecommunications is needed for photonic quantum computers. In addition, the integrated optical cavities of PICs, which resemble wavelength filters when used in classical optics, are suitable for creating quantum states of light known as compressed light, which are valuable for certain types of quantum computing.
Therefore, the concepts of linear optical quantum computing have improved over the past 20 years, and so has the hardware for implementing these concepts.
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