Quantum Representation of Digital Audio

Improving Audio Compression Audio compression is an essential process in music production and distribution, as it allows for smaller file sizes and faster streaming. However, current compression algorithms can lead to a loss of sound quality. Quantum computing could potentially be used to develop more efficient compression algorithms that preserve sound quality while reducing file sizes. The parallel processing power of quantum computing could be particularly useful in this application. By simultaneously processing large amounts of data, quantum computers could potentially identify and eliminate redundancies in the audio data that are not perceptible to the human ear.

Music production and sound engineering are areas that have greatly benefited from advancements in technology, and I believe quantum computing has the potential to revolutionize these fields.

A quantum analogue of data compression has been demonstrated for the first time in the lab. Physicists working in Canada and Japan have squeezed quantum information contained in three quantum bits (qubits) into two qubits.

Compression of classical data is a simple procedure that allows a string of information to take up less space in a computer’s memory. Given an unadulterated string of, for example, 1000 binary values, a computer could simply record the frequency of the 1s and 0s, which might require just a dozen or so binary values. Recording the information about the order of those 1s and 0s would require a slightly longer string, but it would probably still be shorter than the original sequence.

Quantum data are rather different, and it is not possible to simply determine the frequencies of 1s and 0s in a string of quantum information. The problem comes down to the peculiar nature of qubits, which, unlike classical bits, can be a 1, a 0 or some “superposition” of both values.

A user can indeed perform a measurement to record the “one-ness” of a qubit, but such a measurement would destroy any information about that qubit’s “zero-ness”. What is more, if a user then measures a second qubit prepared in an identical way, he or she might find a different value for its “one-ness” – because qubits do not specify unique values but only the probability of measurement outcomes.

“This way you can store the qubits until you know what question you’re interested in,” says Aephraim Steinberg of the University of Toronto. “Then you can measure x if you want to know x; and if you want to know z, you can measure z – whereas if you don’t store the qubits, you have to choose which measurements you want to do right now.”